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Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

Gene Evolution - Fast or Slow?

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...

Gene Flow

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Gene flow is the transfer of genes among populations, resulting from either the dispersal of gametes or from the migration of individuals.

Hardy-Weinberg Principle

Hardy-Weinberg Principle

Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.

Mutation, Gene Flow, and Genetic Drift

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).

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Updated: Jun 4, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

MEGA12: Molecular Evolutionary Genetic Analysis Version 12 for Adaptive and Green Computing.

Sudhir Kumar^1,2, Glen Stecher¹, Michael Suleski¹

¹Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA.

Molecular Biology and Evolution

|December 21, 2024

Summary

This summary is machine-generated.

The new Molecular Evolutionary Genetics Analysis (MEGA12) software significantly speeds up phylogenetic analyses by optimizing model selection and bootstrap tests. This version enhances evolutionary tree inference and identifies fragile clades using sparse learning, improving computational efficiency without sacrificing accuracy.

Keywords:

bootstrap green computing model selection phylogenomics software

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Area of Science:

Computational Biology
Evolutionary Genetics
Bioinformatics

Background:

Phylogenetic analysis is crucial for understanding evolutionary relationships.
Efficient computational methods are needed for large-scale genomic datasets.
Accurate selection of substitution models and robust statistical testing are vital for reliable phylogenetic inference.

Purpose of the Study:

To introduce the 12th version of the Molecular Evolutionary Genetics Analysis (MEGA12) software.
To present significant improvements in computational speed and analytical capabilities.
To enhance the identification of evolutionary patterns and fragile clades in phylogenomic analyses.

Main Methods:

Implementation of heuristics to optimize substitution model selection and bootstrap tests for maximum likelihood (ML) phylogenies.
Integration of an evolutionary sparse learning approach for identifying fragile clades.
Development of fine-grained parallelization for ML analyses and enhancements to the Tree Explorer interface.

Main Results:

Substantial reductions in computational time for phylogenetic analyses using implemented heuristics.
Demonstrated accuracy of results comparable to traditional methods despite time savings.
Successful identification of fragile clades and associated sequences in phylogenomic datasets.

Conclusions:

MEGA12 offers a computationally efficient and accurate platform for molecular evolutionary and phylogenetic analyses.
The new version facilitates more robust inference of evolutionary relationships from large datasets.
MEGA12 provides advanced tools for exploring evolutionary patterns and identifying potentially unstable evolutionary lineages.